Historically, the insurance software market has been dominated by a handful of specialist vendors whose products can be both expensive to deploy and difficult to customize without the involvement of the IT department.
Operated by IT departments largely as “black box” solutions, leading insurance firms are effectively all using the same algorithms to assess and price risk, with little opportunity to fine tune the logic used or to do so quickly in order to, for example, react to market trends and legislative changes, or differentiate insurance products for competitive advantage.
Beyond that, the data analytics and algorithms applied by most legacy applications are, for the most part, basic and well behind the technology curve. The majority are designed to work purely with static data and are unable to handle multiple real-time data streams, or apply advanced predictive models to better understand and forecast possible risk. Added to which the black boxes are failing to add more advanced analytical capabilities, like those available through statistical languages such as R, which is rapidly becoming the tool of choice for data scientists.
Lastly, legacy insurance applications can be difficult to integrate, particularly into a modern IT fabric spanning a mix of public and private cloud, as well as on-premise platforms. That, in turn, creates barriers when it comes to the automation and streamlining of business processes—a growing requirement as companies move towards wider digital transformation.